Degradation Prediction of PEMFCs Using Stacked Echo State Network Based on Genetic Algorithm Optimization

Durability is considered as one of the main technical obstacles to the large-scale commercialization of proton exchange membrane fuel cells (PEMFCs), which can be e.ectively improved through prognostics prediction techniques. This paper proposes a stacked echo state network (ESN) based on the genetic algorithm (GA) to predict the future degradation trend of PEMFCs. By alternately using the projection layer and the encoding layer, the proposed method can make full use of the temporal kernel property of the ESN to encode the multi-scale and multi-level dynamics of the stack voltage, thereby obtaining more robust generalization performance and higher accuracy than existing methods. Specifically, a stack voltage time series of PEMFCs is projected into the high-dimensional echo state space of the reservoir. Then, an auto-encoder projects the echo state representation into the low-dimensional feature space. After that, the genetic algorithm is utilized to optimize the hyperparameters of the developed model. Based on two open-source datasets of PEMFCs with di.erent accelerated test conditions, this paper systematically tested the proposed degradation prediction methods based on di.erent model structures. Test results demonstrate that the proposed method is superior to traditional prediction methods in terms of accuracy and generalization performance.